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Inferential control with the aid of modified QPLS-based soft sensor for an industrial FCCU fractionator

Inferential control with the aid of modified QPLS-based soft sensor for an industrial FCCU fractionator
Inferential control with the aid of modified QPLS-based soft sensor for an industrial FCCU fractionator
A modified quadratic partial least squares (MQPLS) algorithm based on non-linear constrained programming is proposed, in which a sequential unconstrained minimisation technique is employed to calculate the outer input weights and the parameters of inner relationship. Other existing quadratic partial least squares (QPLS) algorithms are also reviewed and compared with the proposed MQPLS in the applications to two datasets, one being an artificial dataset and the other being the real data from an industrial fluidised catalytic cracking unit (FCCU) main fractionator. It is shown that the MQPLS not only can explain better the underlying variability of the data but also achieves improved modelling and predictive performance over the existing QPLS algorithms. An inferential control system is implemented on the distributed control system for an industrial FCCU main fractionator, in which the soft-sensor is built based on the MQPLS algorithm to estimate the diesel oil solidifying point online and the controller is established via a constrained dynamic matrix control algorithm. Experimental results obtained demonstrate that the inferential control system with the aid of the MQPLS soft sensor works much better than the original tray temperature control system and it realises well the bounder control of diesel oil solidifying point.
59-70
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Tu, Ling
1a1ed222-366c-4ac5-a0f3-7277278b6dde
Yang, Minghui
819f5866-45ac-4e1d-a0c6-608e1321037b
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Tu, Ling
1a1ed222-366c-4ac5-a0f3-7277278b6dde
Yang, Minghui
819f5866-45ac-4e1d-a0c6-608e1321037b
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Tian, Xuemin, Tu, Ling, Yang, Minghui and Chen, Sheng (2010) Inferential control with the aid of modified QPLS-based soft sensor for an industrial FCCU fractionator International Journal of Modelling, Identification and Control, 11, (1/2), pp. 59-70.

Record type: Article

Abstract

A modified quadratic partial least squares (MQPLS) algorithm based on non-linear constrained programming is proposed, in which a sequential unconstrained minimisation technique is employed to calculate the outer input weights and the parameters of inner relationship. Other existing quadratic partial least squares (QPLS) algorithms are also reviewed and compared with the proposed MQPLS in the applications to two datasets, one being an artificial dataset and the other being the real data from an industrial fluidised catalytic cracking unit (FCCU) main fractionator. It is shown that the MQPLS not only can explain better the underlying variability of the data but also achieves improved modelling and predictive performance over the existing QPLS algorithms. An inferential control system is implemented on the distributed control system for an industrial FCCU main fractionator, in which the soft-sensor is built based on the MQPLS algorithm to estimate the diesel oil solidifying point online and the controller is established via a constrained dynamic matrix control algorithm. Experimental results obtained demonstrate that the inferential control system with the aid of the MQPLS soft sensor works much better than the original tray temperature control system and it realises well the bounder control of diesel oil solidifying point.

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Published date: 2010
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271521
URI: https://eprints.soton.ac.uk/id/eprint/271521
PURE UUID: d01c00f0-7573-4f75-ba4b-ae1681309d56

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Date deposited: 08 Sep 2010 11:52
Last modified: 18 Jul 2017 06:41

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Contributors

Author: Xuemin Tian
Author: Ling Tu
Author: Minghui Yang
Author: Sheng Chen

University divisions

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